制作
有限元法
熔丝制造
逆向工程
蛋白质丝
材料科学
编码(集合论)
工程制图
结构工程
机械工程
计算机科学
工程类
复合材料
程序设计语言
3D打印
集合(抽象数据类型)
替代医学
病理
医学
作者
S. Ochoa,Santiago Ferrándiz Bou,Luis Garzón,Christian Cobos
出处
期刊:3D printing and additive manufacturing
[Mary Ann Liebert, Inc.]
日期:2024-08-09
卷期号:12 (6): 611-620
被引量:4
标识
DOI:10.1089/3dp.2023.0325
摘要
As additive manufacturing by fused filament fabrication has gained popularity, computational analysis has become fundamental in predicting the mechanical behavior of 3D models. This paper proposes the development of a method for the finite element (FE) simulation of 3D-printed parts, implementing model design reverse engineering using G-code to obtain their digital twins (DTs). Samples were printed under the ASTM D638 standard with different nozzle diameters and layer heights, which allowed them to be mechanically characterized by tensile tests. The tensile tests determined that the diameter of the nozzles used (between 0.2 mm and 1.0 mm) influences the material’s tensile strength. The greater the diameter, the greater the stiffness, which translates into a change in the Young’s modulus, as well as greater tensile strength and thus a reduction of the deformation, for which a value of 2.66 ± 0.6 % was obtained, i.e., the filament diameter did not influence this aspect. After carrying out the reverse engineering process of the samples to obtain DTs of the physical models, the printing G-code was used with the help of a Python script for their conversion to trajectories. These trajectories were introduced into Rhinoceros software with the Grasshopper add-on to obtain the reconstructed 3D models. The deposited filament profile used to reconstruct the DT was obtained by microscopy of the section of the physical samples. The predominant profile observed was that of a flattened oval. FE simulation was then carried out, obtaining a similarity of 90% between the simulated and mechanical tests, which validated the proposed method of predicting mechanical stresses in printed 3D elements.
科研通智能强力驱动
Strongly Powered by AbleSci AI